READING

Hoffer et al. Propose a unsupervised (or more precise self-supervised) training methodology for deep neural networks. Their work is in line with other work trying to learn representation sin a self-supervised fashion. Given an image, the proposed approach called spatial contrasting, takes to patches from the image (one anchor patch) and a random additional patch together with a "contrasting" patch from another image. Then, the goal is to simultaneously maximize the conditional probability $p(f_{anchor}|f_{positive})$ and minimize $p(f_{anchor}|f_{negative}) where $f$ denotes the features computed for the anchor patch, the positive patch and the negative patch. The loss is formed as